Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

The use of distributed energy resources is increasingly being pursued as a supplement and an alternative to large conventional central power stations. The specification of a power-electronic interface is subject to requirements related not only to the renewable energy source itself but also to its effects on the power-system operation, especially where the intermittent energy source constitutes a significant part of the total system capacity. In this paper, new trends in power electronics for the integration of wind and photovoltaic (PV) power generators are presented. A review of the appropriate storage-system technology used for the integration of intermittent renewable energy sources is also introduced. Discussions about common and future trends in renewable energy systems based on reliability and maturity of each technology are presented

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Power-Electronic Systems for the Grid Integration of Renewable Energy Sources: A Survey

Overview of current development in electrical energy storage technologies and the application potential in power system operation

Alternative vehicles, such as plug-in hybrid electric vehicles, are becoming more popular The batteries of these plug-in hybrid electric vehicles are to be charged at home from a standard outlet or on a corporate car park These extra electrical loads have an impact on the distribution grid which is analyzed in terms of power losses and voltage deviations Without coordination of the charging, the vehicles are charged instantaneously when they are plugged in or after a fixed start delay This uncoordinated power consumption on a local scale can lead to grid problems Therefore, coordinated charging is proposed to minimize the power losses and to maximize the main grid load factor The optimal charging profile of the plug-in hybrid electric vehicles is computed by minimizing the power losses As the exact forecasting of household loads is not possible, stochastic programming is introduced Two main techniques are analyzed: quadratic and dynamic programming

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The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid

Demand side management: Benefits and challenges ☆

Preface. 1. Introduction. 1. Why Competition? 2. Dramatis Personae. 3. Models of Competition. 4. Open Questions. 5. Further Reading. 6. Problems. 2. Basic Concepts from Economics. 1. Introduction. 2. Fundamentals of Markets. 3. Concepts from the Theory of the Firm. 4. Types of Markets. 5. Markets with Imperfect Competition. 6. Further Reading. 7. Problems. 3. Markets for Electrical Energy. 1. Introduction. 2. What is the Difference between a Megawatt--hour and a Barrel of Oil? 3. The Need for a Managed Spot Market. 4. Open Electrical Energy Markets. 5. The Managed Spot Market. 6. The Settlement Process. 7. Further Reading. 8. Problems. 4. Participating in Markets for Electrical Energy. 1. Introduction. 2. The Consumer's Perspective. 3. The Producer's Perspective. 4. Perspective of Plants with Very Low Marginal Costs. 5. The Hybrid Participant's Perspective. 6. Further Reading. 7. Problems. 5. System Security and Ancillary Services. 1. Introduction. 2. Describing the needs. 3. Obtaining Ancillary Services. 4. Buying Ancillary Services. 5. Selling Ancillary Services. 6. Further Reading. 7. Problems. 6. Transmission Networks and Electricity Markets. 1. Introduction. 2. Decentralized Trading over a Transmission Network. 3. Centralized Trading over a Transmission Network. 4. Further Reading. 5. Problems. 7. Investing in Generation. 1. Introduction. 2. Generation Capacity from an Investor's Perspective. 3. Generation Capacity from a Customer's Perspective. 4. Further Reading. 5. Problems. 8. Investing in Transmission. 1. Introduction. 2. The Nature of the Transmission Business. 3. Cost--based Transmission Expansion. 4. Value--based Transmission Expansion. 5. Further Reading. 6. Problems. Appendix: Answers to Selected Problems. Abbreviations and Acronyms. Index.

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Fundamentals of power system economics

A concept is presented along with the overarching structure of the virtual power plant (VPP), the primary vehicle for delivering cost efficient integration of distributed energy resources (DER) into the existing power systems. The growing pressure, primarily driven by environmental concerns, for generating more electricity from renewables and improving energy efficiency have promoted the application of DER into electricity systems. So far, DER have been used to displace energy from conventional generating plants but not to displace their capacity as they are not visible to system operators. If this continues, this will lead to problematic over-capacity issues and under-utilisation of the assets, reduce overall system efficiency and eventually increase the electricity cost that needs to be paid by society. The concept of VPP was developed to enhance the visibility and control of DER to system operators and other market actors by providing an appropriate interface between these system components. The technical and commercial functionality facilitated through the VPP are described and concludes with case studies demonstrating the benefit of aggregation (VPP concept) and the use of the optimal power flow algorithm to characterise VPP

Virtual power plant and system integration of distributed energy resources

Because of the introduction of competition in the electricity supply industry, it has become much more important to be able to determine which generators are supplying a particular load, how much use each generator is making of a transmission line and what is each generator's contribution to the system losses. This paper describes a technique for answering these questions which is not limited to incremental changes and which is applicable to both active and reactive power. Starting from a power flow solution, the technique first identifies the busses which are reached by power produced by each generator. Then it determines the sets of buses supplied by the same generators. Using proportionality assumption, it is then possible to calculate the contribution of each generator to the loads and flows. The applicability of the proposed technique is demonstrated using a 30-bus example.

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Goran Strbac

Papers

Demand side management: Benefits and challenges ☆

Fundamentals of power system economics

Virtual power plant and system integration of distributed energy resources

Contributions of individual generators to loads and flows

Fundamentals of Power System Economics: Kirschen/Power System Economics